Upload README.md

README.md

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 license: apache-2.0
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+# DiagramQG Dataset
+
+![Dataset Examples](example.pdf)
+*Figure 1: Four different examples of different subjects in DiagramQG dataset.*
+
+![Domain Distribution](course.pdf)
+*Figure 2: Domain diversity in DiagramQG. Each color corresponds to one subject: Natural Science (blue), Earth Science (yellow), Applied Science (green), and Social Science (orange).*
+
+## Overview
+DiagramQG is a comprehensive educational dataset focused on scientific diagram question generation. It contains:
+
+- 19,475 unique questions
+- 8,372 diagrams  
+- 44,472 combinations of (target & concept text constraint, diagram, question)
+- Coverage across 4 subjects, 15 courses, and 169 concepts
+
+## Dataset Structure
+
+### Subject Areas
+The dataset covers four main subject areas:
+- Natural Science
+- Earth Science  
+- Applied Science
+- Social Science
+
+### Hierarchical Organization
+Data is organized hierarchically:
+1. Subject (e.g., Natural Science)
+2. Course (e.g., Biology)  
+3. Concept (e.g., Ecological interactions)
+
+## Data Collection Process
+
+### Phase 1: Initial Data Gathering
+- Sources: Existing datasets and Google Image Search
+- Raw dataset: 20,000+ diagrams and 40,000+ questions
+
+### Phase 2: Organization
+- Classification into 4 subjects and 15 courses
+- Mapping questions to 169 distinct concepts
+
+### Phase 3: Annotation
+- Trained crowd workers annotate:
+  - Target & concept text constraints
+  - Diagram elements and texts
+- Produced 70,000+ unique combinations
+
+### Phase 4: Quality Assurance
+- Secondary crowd worker evaluation (0-100 scale)
+- Filtered combinations below 60 points
+- Final dataset: 44,472 validated combinations
+
+## Dataset Analysis
+
+### Question Distribution
+![Question Distribution](sunburst_chart_hd.png)
+*Figure 3: Question distribution in DiagramQG.*
+
+### Concept Distribution
+![Concept Distribution](proportions_plot_v6.png)
+*Figure 4: Distribution of diagrams, questions, and questions per diagram ratios across different concepts in DiagramQG.*
+
+### Dataset Comparison
+| Dataset | Questions | Images | Objects/Image | Image Type | Constraints | Knowledge Type |
+|---------|-----------|---------|---------------|------------|-------------|----------------|
+| VQAv2.0 | 1.1M | 20k | 3.5 | natural | answer | N/A |
+| FVQA | 5,826 | 2k | 2.9 | natural | answer | common-sense |
+| VQG-COCO | 25,000 | 5k | 3.3 | natural | image, caption | common-sense |
+| K-VQG | 16,098 | 13K | 2.7 | natural | knowledge triple | common-sense |
+| DiagramQG | 19,475 | 8,372 | 11.2 | diagram | target, concept | subject knowledge |
+
+## Unique Challenges
+
+1. **Domain-specific Knowledge Requirement**
+   - Requires understanding of specialized subject concepts
+   - Goes beyond common sense reasoning
+
+2. **Long-tail Distribution**
+   - Uneven concept coverage
+   - Challenges in model generalization
+
+3. **High Information Density**
+   - Complex diagram interpretation
+   - Dense visual information processing